diff --git "a/Kaggle-SD-Webui v2.5.2(最新).ipynb" "b/Kaggle-SD-Webui v2.5.2(最新).ipynb"
new file mode 100644--- /dev/null
+++ "b/Kaggle-SD-Webui v2.5.2(最新).ipynb"
@@ -0,0 +1 @@
+{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"\n## NovelAi Stable Diffusion - webui AI绘画项目 SD云部署\n**torch: 2.0.0+cu118  •  xformers: 0.0.21**\n## <span style=\"color:red; font-weight:bold;\"> 希望有能力的大佬可以宣传本项目！全网唯一全免费云端部署</span>\n<!--- \n这里是作者的署名和群号码，未经同意请勿删除，删一句家里人少一个\n--->\n<!--- \n倒卖狗全家死光\n--->\n","metadata":{}},{"cell_type":"markdown","source":"### 请先看使用教程！不看的话运行出错不怪我，如果你是手机端请用Edge浏览器并且调成电脑版UI\n> ## 使用Run All或者Save Version运行，在输出结果中找到Public URL从公网链接进入webui，进入如图所示的界面就说明成功了\n![image.png](attachment:a958cdd4-ac25-4b31-be41-4398b9df625e.png)","metadata":{},"attachments":{"a958cdd4-ac25-4b31-be41-4398b9df625e.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"## 感谢 [Yiyiooo](https://www.kaggle.com/code/yiyiooo/stable-diffusion-webui-novelai)为开发做出的贡献\n## 未经同意禁止将源代码用于出售，一经发现必定追究\n<!--- \n这里是作者的署名和群号码，未经同意请勿删除，删一句家里人少一个\n--->\n<!--- \n倒卖狗全家死光\n--->\n","metadata":{}},{"cell_type":"markdown","source":"## Ai绘画模型下载站：\n## [Civitai](http://civitai.com) （C站）\n \n### [huggingface](http://huggingface.co)\n# 友情合作 \n###   [pix.ink](http://pix.ink) # 片绘","metadata":{}},{"cell_type":"markdown","source":"----","metadata":{}},{"cell_type":"markdown","source":"# > <span style=\"color:green; font-weight:;\"> Webui基础配置 </span>","metadata":{}},{"cell_type":"code","source":"# True 表示是 ， False 表示否\n# 安装目录\ninstall_path=\"/kaggle/working\" #或者/kaggle\nupdata_webui = False  #是否开机自动更新webui\n\n# 重置变量 会删掉sd_webui重新安装\nreLoad = True\nupdata_webui = False\n\n#清理和打包生成的图片\nzip_output=True\nclear_output=True\n#打包环境减少下次启动时\nuse_zip_venv = False\n\n\n# 使用huggingface保存和载入webui配置文件\nhuggingface_use = True\nhuggingface_token_file = '/kaggle/input/tenkens/hugfacetoken.txt'\nhuggiingface_repo_id = 'ACCA225/sd-configs-4'\n# 将会同步的文件\nyun_files = [\n'ui-config.json',\n'config.json',\n'styles.csv'\n]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"------","metadata":{}},{"cell_type":"markdown","source":"# > <span style=\"color:green; font-weight:;\">插件，模型地址 （添加模型在此代码单元格修改）</span>‘\n## <span style=\"color:red; font-weight:bold;\">上传模型方法见顶部的教程，C站获取下载链接方法：在模型下载处右键，手机端用户长按下载按钮，复制地址即可</span>\n## <span style=\"color:red; font-weight:bold;\">如果模型数量较多，可选用上传到kaggle数据集方法来载入模型</span>","metadata":{}},{"cell_type":"code","source":"#模型和插件，下载的东西越多，启动速度更慢\n\n是否启用ControlNet = False #  开启后需要多花费2-3分钟来下载基本模型\n是否启用SadTalker = False # 虚拟数字人插件，下载特定模型要花费1分钟时间，生成的视频保存在sd目录下的/results文件夹里\n# 其它插件列表： git仓库地址\n# 不需要的插件在前面加 # ，插件地址之间需要用英语逗号隔开\nextensions = [\n    'https://github.com/Elldreth/loopback_scaler',\n    'https://github.com/jexom/sd-webui-depth-lib',\n    'https://github.com/AlUlkesh/stable-diffusion-webui-images-browser', #图库浏览器\n    #'https://github.com/camenduru/sd-civitai-browser', #C站助手\n    #'https://github.com/Mikubill/sd-webui-controlnet', #控制网插件，神器！！\n    'https://github.com/nonnonstop/sd-webui-3d-open-pose-editor', # 3D openpose，可以让你的老婆摆出你想要的姿势\n    'https://github.com/2575044704/stable-diffusion-webui-localization-zh_CN2', #汉化\n    'https://github.com/opparco/stable-diffusion-webui-two-shot', #潜变量成对\n    #'https://github.com/minicacas/stable-diffusion-webui-composable-lora',\n    'https://github.com/DominikDoom/a1111-sd-webui-tagcomplete', #tag自动补全\n    #'https://github.com/pkuliyi2015/multidiffusion-upscaler-for-automatic1111', #分块vae\n    #'https://github.com/KohakuBlueleaf/a1111-sd-webui-locon',\n    'https://github.com/hnmr293/sd-webui-cutoff', #Cutoff\n    'https://github.com/hako-mikan/sd-webui-lora-block-weight', #Lora分层\n    'https://github.com/butaixianran/Stable-Diffusion-Webui-Civitai-Helper', #C站助手\n    'https://github.com/catppuccin/stable-diffusion-webui', #UI修改，推荐\n    #'https://github.com/Nevysha/Cozy-Nest',\n    #'https://github.com/Scholar01/sd-webui-mov2mov', #AI视频转视频\n    #'https://github.com/toriato/stable-diffusion-webui-wd14-tagger', #WD14打标器\n    #'https://github.com/KohakuBlueleaf/a1111-sd-webui-lycoris', #LyCORIS插件，Lora升级版\n    'https://github.com/deforum-art/sd-webui-deforum', #Deform，AI视频\n    #'https://github.com/zanllp/sd-webui-infinite-image-browsing', #云端用不了\n    'https://github.com/vladmandic/sd-extension-system-info',  #系统信息\n    '\thttps://github.com/d8ahazard/sd_dreambooth_extension', #Dreambooth训练\n    #'https://github.com/viyiviyi/prompts-filter'\n]\n\n# Stable Diffusion模型数据集请放在这里（只填模型的目录即可）\nsd_model = [\n'/kaggle/input/9527-fp16',\n            ]\n# Stable Diffusion模型（Checkpoint）下载链接放这里\nsd_model_urls=[\n# majic Realistic\n'https://civitai.com/api/download/models/94640',\n# null style v2\n'https://huggingface.co/swl-models/NullStyle-v2.0/resolve/main/NullStyle-v2.0.safetensors'\n]\n\n# VAE模型请放在这里（不用填模型的文件名，只填模型的目录即可）\nvae_model = []\n#VAE模型下载链接放这里\n# 注意SDXL类模型的VAE不能与SD1.5的VAE混用，这是常识！\nvae_model_urls=[\n'https://huggingface.co/WarriorMama777/OrangeMixs/resolve/main/VAEs/orangemix.vae.pt',\n'https://civitai.com/api/download/models/28569',\n'https://civitai.com/api/download/models/87822', # 画真人专用的VAE模型\n]\n\n# Lora模型的数据集路径请写在这里：\nlora_model = [\n#'/kaggle/input/lora-1',\n] \n# Lora模型下载链接放这里\nlora_model_urls=[\n#墨心\n'https://civitai.com/api/download/models/14856',\n#山楂糕\n'https://civitai.com/api/download/models/41580',\n#细节调整\n'https://civitai.com/api/download/models/62833'\n]\n# Lycoris和loha模型的数据集路径请写在这里：\nlyco_model = [\n#'/kaggle/input/lora-1',\n] \n# Lycoris和loha模型下载链接放这里\nlyco_model_urls=[\n#FilmGirl 胶片风\n'https://civitai.com/api/download/models/75069',\n#Teacher clothes 教师衣服\n\"https://civitai.com/api/download/models/65426\",\n#伪日光\n'https://civitai.com/api/download/models/71235',\n]\n\n# ControlNet模型data请放在这里：\ncn_model = [\n]\n# controlnet模型下载链接放这里\ncn_model_urls = [\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_ip2p_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11e_sd15_shuffle_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11f1p_sd15_depth_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_canny_fp16.safetensors', #硬边缘检测\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_inpaint_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_lineart_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_mlsd_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_normalbae_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_openpose_fp16.safetensors', #姿态检测\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_scribble_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15_softedge_fp16.safetensors',\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11p_sd15s2_lineart_anime_fp16.safetensors', #线稿\n'https://huggingface.co/comfyanonymous/ControlNet-v1-1_fp16_safetensors/resolve/main/control_v11u_sd15_tile_fp16.safetensors', #分块\n'https://huggingface.co/DionTimmer/controlnet_qrcode-control_v1p_sd15/resolve/main/control_v1p_sd15_qrcode.safetensors', # 艺术二维码(神器！！)\n]\n\n# Hypernetworks超网络模型路径请放在这里：\nhypernetworks_model = []\n#Hypernetworks超网络模型下载链接请放在这里\nhypernetworks_model_urls = []\n\n#放大算法路径请放在这里\nESRGAN = []\n#放大算法链接请放在这里\nESRGAN_urls = [\n'https://huggingface.co/FacehugmanIII/4x_foolhardy_Remacri/resolve/main/4x_foolhardy_Remacri.pth',\n'https://huggingface.co/konohashinobi4/4xAnimesharp/resolve/main/4x-AnimeSharp.pth',\n'https://huggingface.co/lokCX/4x-Ultrasharp/resolve/main/4x-UltraSharp.pth',\n]\n\n# embeddings（pt文件）请放在这里:\nembeddings_model = [\n'/kaggle/input/bad-embedding',\n] \n# embeddings（pt文件）下载链接请放在这里:\nembeddings_model_urls=[\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/%E4%BA%BA%E4%BD%93%E4%BF%AE%E6%AD%A3/EasyNegative.pt',\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/%E4%BA%BA%E4%BD%93%E4%BF%AE%E6%AD%A3/bad-artist-anime.pt',\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/%E4%BA%BA%E4%BD%93%E4%BF%AE%E6%AD%A3/bad-hands-5.pt',\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/%E4%BA%BA%E4%BD%93%E4%BF%AE%E6%AD%A3/bad_prompt_version2.pt',\n'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/%E4%BA%BA%E4%BD%93%E4%BF%AE%E6%AD%A3/bad-image-v2-39000.pt',\n'https://huggingface.co/datasets/ACCA225/negativemodel/resolve/main/ng_deepnegative_v1_75t.pt',\n'https://huggingface.co/datasets/ACCA225/negativemodel/resolve/main/badhand-v4.pt',\n''\n]\n\n#script文件导入\nscripts = []\n#script文件下载链接导入\nscripts_urls = [\n#'https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/repositories/k-diffusion/k_diffusion/sampling.py'\n]\n\n#tag词库文件导入\ntags = []\n#tag词库文件下载链接导入\ntags_urls=[\n\"https://huggingface.co/datasets/sukaka/sd_configs/resolve/main/danbooru.zh_CN.csv\",\n]\n\n#'''说明 : 下载代码在download_model()函数里，如果需要添加其它模型下载地址和路径，请自行修改代码'''","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"------","metadata":{}},{"cell_type":"markdown","source":"# > <span style=\"color:green; font-weight:;\"> 内网穿透，Webui启动参数设置</span>","metadata":{}},{"cell_type":"code","source":"#ngrok穿透\nngrok_use = True\nngrokTokenFile='/kaggle/input/tenkens/Authtoken.txt' # 非必填 存放ngrokToken的文件的路径\n#Frp 内网穿透\nuse_frpc = False\nfrpconfigfile = '/kaggle/input/tenkens/7860.ini'  # 非必填 frp 配置文件，本地端口 7860\n#ready-squids-thank.loca.lt 内网穿透 (推荐)\nlocaltunnel = True\n# 启动时默认加载的模型名称 填模型名称，名称建议带上文件名后缀\nusedCkpt = 'NullStyle-v2.0.safetensors'\n\n'''\n可选的启动参数见笔记的最底部附录！！！！这里与秋叶的启动器不同的是，这里的启动参数需要你自己填上去，附录中每个启动参数都有对应作用\n'''\n#启动参数（args）\nargs = [\n    '--share', #开启公网访问，不开启的话没有gradio链接\n    '--xformers', # 强制使用 xformers 优化\n    '--lowram', #低内存优化\n    '--no-hashing', #取消模型哈希计算值，加快启动速度\n    '--disable-nan-check', #取消Nan检查\n    '--enable-insecure-extension-access', #强制允许在webui使用安装插件，即使开启了--share\n    '--disable-console-progressbars', \n    '--enable-console-prompts', #开启控制台显示prompt\n    '--no-gradio-queue',\n    '--no-half-vae', #VAE开启全精度\n    '--api', #搭建QQ画图机器人或者开AI画图网站接入SD要开启这个\n    #'--listen',  # 在Kaggle里没用，将127.0.0.1:7860变成0.0.0.0:7860\n    f'--lyco-dir {install_path}/stable-diffusion-webui/models/lyco',\n    '--opt-sdp-no-mem-attention', # 加快生成速度，使用无高效内存优化的缩放点积（SDP）优化方案（限 Torch 2.x）, 属于 Cross-Attention优化方案的一种，不能与--opt-sdp-attention混合使用\n    '--opt-split-attention', # Cross attention layer optimization内存优化方案\n]\n\n\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"-------","metadata":{}},{"cell_type":"markdown","source":"# > <span style=\"color:green; font-weight:;\">Webui 双开设置</span>","metadata":{}},{"cell_type":"code","source":"use2 = False #是否开启两个webui， Kaggle的GPU选项必须是 T4 x2， 使用两张卡一起跑图","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"'''\nuse2必须设置为True下列配置才生效\n'''\n#ngrok穿透\nngrok_use1 = True\nngrokTokenFile1='/kaggle/input/tenkens/Authtoken1.txt' # 非必填 存放ngrokToken的文件的路径\n#Frp 内网穿透\nuse_frpc1 = False\nfrpconfigfile1 = '/kaggle/input/tenkens/7861.ini'  # 非必填 frp 配置文件，本地端口 7860\n\n#第二个webui使用的模型\nusedCkpt1 = 'cetusMix_Coda2.safetensors'\n\n#启动参数\nargs1 = [\n    '--share',\n    '--xformers',\n    '--lowram',\n    '--no-hashing',\n    '--disable-nan-check',\n    '--enable-insecure-extension-access',\n    '--disable-console-progressbars',\n    '--enable-console-prompts',\n    '--no-gradio-queue',\n    '--no-half-vae',\n    '--api',\n    f'--lyco-dir {install_path}/stable-diffusion-webui/models/lyco',\n    '--opt-sdp-attention',\n    '--opt-split-attention'\n]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# > <span style=\"color:green; font-weight:;\">功能函数，请勿更改</span>","metadata":{}},{"cell_type":"code","source":"#使用的库\nfrom pathlib import Path\nimport subprocess\nimport pandas as pd\nimport shutil\nimport os\nimport time\nimport re\nimport gc\nimport requests\nimport zipfile\nimport threading\nimport time\nimport socket\nfrom concurrent.futures import ProcessPoolExecutor\nos.environ['install_path'] = install_path","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#功能函数，内存优化\ndef libtcmalloc():\n    if os.path.exists('/kaggle/temp'):\n        os.chdir('/kaggle')\n        os.chdir('temp')\n        os.environ[\"LD_PRELOAD\"] = \"libtcmalloc.so\"\n        print('内存优化已安装')\n    else:\n        \n        if use_frpc:\n            !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/datasets/ACCA225/Frp/resolve/main/frpc -d /kaggle/working/frpc -o frpc\n        os.system('pip install -q pyngrok ')\n        os.chdir('/kaggle')\n        os.makedirs('temp', exist_ok=True)\n        os.chdir('temp')\n        os.system('wget -qq  http://launchpadlibrarian.net/367274644/libgoogle-perftools-dev_2.5-2.2ubuntu3_amd64.deb')\n        os.system('wget -qq  https://launchpad.net/ubuntu/+source/google-perftools/2.5-2.2ubuntu3/+build/14795286/+files/google-perftools_2.5-2.2ubuntu3_all.deb')\n        os.system('wget -qq  https://launchpad.net/ubuntu/+source/google-perftools/2.5-2.2ubuntu3/+build/14795286/+files/libtcmalloc-minimal4_2.5-2.2ubuntu3_amd64.deb')\n        os.system('wget -qq  https://launchpad.net/ubuntu/+source/google-perftools/2.5-2.2ubuntu3/+build/14795286/+files/libgoogle-perftools4_2.5-2.2ubuntu3_amd64.deb')\n        os.system('apt install -qq libunwind8-dev -y')\n        !dpkg -i *.deb\n        os.environ[\"LD_PRELOAD\"] = \"libtcmalloc.so\"\n        !rm *.deb\n        print('内存优化已安装')\n        ","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pynvml\n\ndef get_gpu_temperature():\n    pynvml.nvmlInit()\n    \n    # 获取 GPU 数量\n    device_count = pynvml.nvmlDeviceGetCount()\n    \n    temperatures = []\n    \n    for i in range(device_count):\n        handle = pynvml.nvmlDeviceGetHandleByIndex(i)\n        \n        # 获取温度\n        temperature = pynvml.nvmlDeviceGetTemperature(handle, pynvml.NVML_TEMPERATURE_GPU)\n        \n        temperatures.append(temperature)\n    \n    pynvml.nvmlShutdown()\n    \n    return temperatures\n\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"-------------------","metadata":{}},{"cell_type":"markdown","source":"# > <span style=\"color:green; font-weight:;\">下载函数，请勿更改</span>","metadata":{}},{"cell_type":"code","source":" import os\n import re\n def putDownloadFile(url:str,distDir:str,file_name:str=None):\n     if re.match(r'^[^:]+:(https?|ftps?)://', url, flags=0):\n         file_name = re.findall(r'^[^:]+:',url)[0][:-1]\n         url = url[len(file_name)+1:]\n     if not re.match(r'^(https?|ftps?)://',url):\n         return\n     file_name = re.sub(r'\\s+','_',file_name or '')\n     dir = str(hash(url)).replace('-','')\n     down_dir = f'{install_path}/down_cache/{dir}'\n     !mkdir -p {down_dir}\n     return [url,file_name,distDir,down_dir]\n\n def get_file_size_in_gb(file_path):\n     size_in_bytes = Path(file_path).stat().st_size\n     size_in_gb = size_in_bytes / (1024 ** 3)\n     return '%.2f' % size_in_gb\n    \n\n def startDownloadFiles(download_list):\n     print('下��列表:\\n','\\n'.join([f'{item[0]} -> {item[2]}/{item[1]}' for item in download_list]))\n     dist_list = []\n     for dow_f in download_list:\n         !mkdir -p {dow_f[3]}\n         print('下载 名称：',dow_f[1],'url：',dow_f[0])\n         output_file = f' -O {dow_f[3]}/{dow_f[1]}'\n         if len(os.listdir(dow_f[3])) > 0:\n             continue\n         os.system(f\"wget {dow_f[0]} --tries=3 --timeout=60 -P {dow_f[3]} {output_file if len(dow_f[1]) > 0 else ''} -o {install_path}/down_cache/log.log\")\n         if len(os.listdir(dow_f[3])) == 0:\n             print('下载出错：',dow_f[0])\n             continue\n         file_name = os.listdir(dow_f[3])[0]\n         !mkdir -p {dow_f[2]}\n         down_file_path = f'{dow_f[3]}/{file_name}'\n         if Path(down_file_path).is_symlink():\n             down_file_path = os.readlink(down_file_path)\n             print('文件真实地址：'+down_file_path)\n         if not Path(down_file_path).exists():\n             print('文件异常')\n             continue\n         print(f'文件大小：{get_file_size_in_gb(down_file_path)}G')\n         dist_path = f'{dow_f[2]}/{file_name}'\n         dist_path = dist_path.replace('%20',' ').strip().replace(' ','_')\n         print(f'移动文件 {down_file_path} -> {dist_path}')\n         os.system(f'ln -f \"{down_file_path}\" \"{dist_path}\"')\n         if dow_f[2] not in dist_list:\n             dist_list.append(dow_f[2])\n     for dist_dir in dist_list:\n         print(dist_dir,os.listdir(dist_dir))\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### > <span style=\"color:green; font-weight:;\">SD download & venv Download ： version: v1.4.0 • python: 3.10.6 • torch: 2.0.1+cu118 • xformers: 0.0.20</span>","metadata":{}},{"cell_type":"code","source":"\ndef unzip_file(src: str, dest: str = '/kaggle/outputs'):\n    if os.path.exists(src):\n        with zipfile.ZipFile(src, 'r') as zip_ref:\n            for member in zip_ref.namelist():\n                filename = os.path.basename(member)\n                if not filename:\n                    continue\n                dest_file = os.path.join(dest, filename)\n                if os.path.exists(dest_file):\n                    os.remove(dest_file)\n                zip_ref.extract(member, dest)\n\ndef webui_config_download(yun_files, huggiingface_repo_id):\n    %cd $install_path/stable-diffusion-webui/\n    for yun_file in yun_files:\n        url = f'https://huggingface.co/datasets/{huggiingface_repo_id}/resolve/main/{yun_file}'\n        response = requests.head(url)\n        if response.status_code == 200:\n            result = subprocess.run(['wget', '-O', yun_file, url, '-q'], capture_output=True)\n            if result.returncode != 0:\n                print(f'Error: Failed to download {yun_file} from {url}')\n        else:\n            print(f'Error: Invalid URL {url}')\ninstall_path2 = '/kaggle/working/opt/conda/envs/'\nVenvpath = '/kaggle/input/sd-webui-venv/venv.tar.bak' \ndef venv_install():\n    if os.path.exists(Venvpath):\n        if os.path.exists('/kaggle/working/opt'):\n            !source /kaggle/working/opt/conda/envs/venv/bin/activate venv\n            print('环境安装完毕')\n        else:\n            os.makedirs(install_path2, exist_ok=True)\n            %cd {install_path2}\n            !mkdir venv\n            print('安装VENV环境')\n            !tar -xf {Venvpath} -C {install_path2}venv\n            !source /kaggle/working/opt/conda/envs/venv/bin/activate venv\n            print('环境安装完毕')\n    else:\n        %cd /opt/conda/envs\n        if os.path.exists('venv'):\n            print('环境已安装')\n        else:\n            %cd /kaggle/working/\n            if not os.path.exists('venv.tar.gz'):\n                print('下载 venv')\n                !aria2c --console-log-level=error -c -x 16 -s 16 -k 1M https://huggingface.co/datasets/sukaka/venv_ai_drow/resolve/main/sd_webui/sd_webui_torch201_cu118_xf20.tar.gz -o venv.tar.gz\n            print('successfully downloaded venv.tar.gz')\n            %cd /opt/conda/envs/\n            !mkdir venv\n            %cd venv\n            print('installing venv')\n            os.system('apt -y install -qq pigz > /dev/null 2>&1')\n            !pigz -dc -p 5 /kaggle/working/venv.tar.gz | tar xf -\n            !source /opt/conda/bin/activate venv\n            print('环境安装完毕')\n\n\ndef install_webui():\n    %cd $install_path\n    if reLoad:\n        !rm -rf stable-diffusion-webui\n    if Path(\"stable-diffusion-webui\").exists():\n        if updata_webui:\n            %cd $install_path/stable-diffusion-webui/\n            !git pull\n    else:\n        os.system('git clone https://github.com/PNuwa/stable-diffusion-webui-v1.6.0.git > /dev/null 2>&1')\n        os.system('mv /kaggle/working/stable-diffusion-webui-v1.6.0 -- /kaggle/working/stable-diffusion-webui')\n        %cd $install_path/stable-diffusion-webui/\n        #!wget https://huggingface.co/datasets/ACCA225/sdconfig3/blob/main/blocked_prompts.txt\n        with open('launch.py', 'r') as f:\n            content = f.read()\n        with open('launch.py', 'w') as f:\n            f.write('import ssl\\n')\n            f.write('ssl._create_default_https_context = ssl._create_unverified_context\\n')\n            f.write(content)\n    if huggingface_use:\n        webui_config_download(yun_files, huggiingface_repo_id)\n    \n    unzip_file('/kaggle/working/图片.zip')\n    install_extensions(install_path, extensions)\n    download_model()\n    link_models()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### > <span style=\"color:green; font-weight:;\">旧版下载代码</span>","metadata":{}},{"cell_type":"code","source":"from concurrent.futures import ThreadPoolExecutor\n# 安装插件，下载和同步模型\ndef install_extensions(install_path, extensions):\n    print('安装插件，此处出现红条是正常的')\n    os.chdir(os.path.join(install_path, 'stable-diffusion-webui'))\n    os.makedirs('extensions', exist_ok=True)\n    os.chdir('extensions')\n    if 是否启用ControlNet:\n        !git clone https://github.com/Mikubill/sd-webui-controlnet\n    if 是否启用SadTalker:\n        !git clone https://github.com/OpenTalker/SadTalker\n        !mkdir -p SadTalker\n        %cd SadTalker\n        !bash <(wget -qO- https://raw.githubusercontent.com/Winfredy/SadTalker/main/scripts/download_models.sh)\n        %cd ..\n    def clone_repo(ex):\n        repo_name = ex.split('/')[-1]\n        if not os.path.exists(repo_name):\n            os.system('git clone ' + ex)\n\n    with ThreadPoolExecutor(max_workers=99) as executor:\n        executor.map(clone_repo, extensions)\n\ndef download_link(link, target_folder):\n    if link.startswith('https://huggingface.co/'):\n        filename = re.search(r'[^/]+$', link).group(0)\n        print('下载模型')\n        return f'aria2c --console-log-level=error -c -x 16 -s 16 -k 1M -d \"{target_folder}\" -o \"{filename}\" \"{link}\"'\n    else:\n        return f'aria2c --console-log-level=error -c -x 16 -s 16 -k 1M --remote-time -d \"{target_folder}\" \"{link}\"'\n\ndef download_links(links, target_folder):\n    tasks = []\n    for link in links:\n        tasks.append(download_link(link, target_folder))\n    return tasks\n\ndef download_links_all(tasks):\n    with ThreadPoolExecutor(max_workers=5) as executor:\n        for task in tasks:\n            executor.submit(os.system, task)\n        \n# 下载模型文件\ndef download_model():\n    os.chdir('/kaggle')\n    os.makedirs('models', exist_ok=True)\n    os.chdir('models')\n    os.makedirs('VAE', exist_ok=True)\n    os.makedirs('Stable-diffusion', exist_ok=True)\n    os.makedirs('Lora', exist_ok=True)\n    os.makedirs('cn-model', exist_ok=True)\n    os.makedirs('hypernetworks', exist_ok=True)\n    os.makedirs('ESRGAN', exist_ok=True)\n    os.makedirs('lyco', exist_ok=True)\n    tasks = []\n    tasks.extend(download_links(vae_model_urls, 'VAE'))\n    tasks.extend(download_links(sd_model_urls, 'Stable-diffusion'))\n    tasks.extend(download_links(lora_model_urls, 'Lora'))\n    if 是否启用ControlNet:\n        tasks.extend(download_links(cn_model_urls, 'cn-model'))\n    tasks.extend(download_links(hypernetworks_model_urls, 'hypernetworks'))\n    tasks.extend(download_links(ESRGAN_urls, 'ESRGAN'))\n    tasks.extend(download_links(lyco_model_urls, 'lyco'))\n    tasks.extend(download_links(embeddings_model_urls, f'{install_path}/stable-diffusion-webui/embeddings'))\n    tasks.extend(download_links(scripts_urls, f'{install_path}/stable-diffusion-webui/scripts'))\n    tasks.extend(download_links(tags_urls, f'{install_path}/stable-diffusion-webui/extensions/a1111-sd-webui-tagcomplete/tags'))\n    download_links_all(tasks)\n\n\ndef create_symlinks(folder_paths, target_dir):\n    print('链接模型中')\n    # Create target directory if it doesn't exist\n    if not os.path.exists(target_dir):\n        os.makedirs(target_dir)\n    # Remove broken symlinks in target directory\n    for filename in os.listdir(target_dir):\n        target_path = os.path.join(target_dir, filename)\n        if os.path.islink(target_path) and not os.path.exists(target_path):\n            os.unlink(target_path)\n    # Create new symlinks\n    for source_path in folder_paths:\n        if not os.path.exists(source_path):\n            continue\n        if os.path.isdir(source_path):\n            for filename in os.listdir(source_path):\n                source_file_path = os.path.join(source_path, filename)\n                target_file_path = os.path.join(target_dir, filename)\n                if not os.path.exists(target_file_path):\n                    os.symlink(source_file_path, target_file_path)\n                    print(f'Created symlink for {filename} in {target_dir}')\n        else:\n            filename = os.path.basename(source_path)\n            target_file_path = os.path.join(target_dir, filename)\n            if not os.path.exists(target_file_path):\n                os.symlink(source_path, target_file_path)\n                print(f'Created symlink for {filename} in {target_dir}')\n    print('链接成功')\n    \n# 链接模型文件\ndef link_models():\n    cn_model.append('/kaggle/models/cn-model')\n    vae_model.append('/kaggle/models/VAE')\n    sd_model.append('/kaggle/models/Stable-diffusion')\n    lora_model.append('/kaggle/models/Lora')\n    hypernetworks_model.append('/kaggle/models/hypernetworks')\n    ESRGAN.append('/kaggle/models/ESRGAN')\n    lyco_model.append('/kaggle/models/lyco')\n    \n    create_symlinks(vae_model,f'{install_path}/stable-diffusion-webui/models/VAE')\n    create_symlinks(sd_model,f'{install_path}/stable-diffusion-webui/models/Stable-diffusion')\n    create_symlinks(lora_model,f'{install_path}/stable-diffusion-webui/models/Lora')\n    create_symlinks(cn_model,f'{install_path}/stable-diffusion-webui/extensions/sd-webui-controlnet/models')\n    create_symlinks(embeddings_model,f'{install_path}/stable-diffusion-webui/embeddings')\n    create_symlinks(hypernetworks_model,f'{install_path}/stable-diffusion-webui/models/hypernetworks')\n    create_symlinks(ESRGAN,f'{install_path}/stable-diffusion-webui/models/ESRGAN')\n    create_symlinks(tags,f'{install_path}/stable-diffusion-webui/extensions/a1111-sd-webui-tagcomplete/tags')\n    create_symlinks(scripts,f'{install_path}/stable-diffusion-webui/scripts')\n    create_symlinks(lyco_model,f'{install_path}/stable-diffusion-webui/models/lyco')\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"----","metadata":{}},{"cell_type":"markdown","source":"### > <span style=\"color:green; font-weight:;\">Ngrok，FRP内网穿透</span>","metadata":{}},{"cell_type":"code","source":"# 功能函数：内网穿透\n#ngrok\ndef ngrok_start(ngrokTokenFile: str, port: int, address_name: str, should_run: bool):\n    if not should_run:\n        print('Skipping ngrok start')\n        return\n    if Path(ngrokTokenFile).exists():\n        with open(ngrokTokenFile, encoding=\"utf-8\") as nkfile:\n            ngrokToken = nkfile.readline()\n        print('use nrgok')\n        from pyngrok import conf, ngrok\n        conf.get_default().auth_token = ngrokToken\n        conf.get_default().monitor_thread = False\n        ssh_tunnels = ngrok.get_tunnels(conf.get_default())\n        if len(ssh_tunnels) == 0:\n            ssh_tunnel = ngrok.connect(port, bind_tls=True)\n            print(f'{address_name}：' + ssh_tunnel.public_url)\n        else:\n            print(f'{address_name}：' + ssh_tunnels[0].public_url)\n    else:\n        print('skip start ngrok')\n\n#Frp内网穿透 \nimport subprocess\n\ndef install_Frpc(port, frpconfigfile, use_frpc):\n    if use_frpc:\n        subprocess.run(['chmod', '+x', '/kaggle/working/frpc/frpc'], check=True)\n        print(f'正在启动frp ，端口{port}')\n        subprocess.Popen(['/kaggle/working/frpc/frpc', '-c', frpconfigfile])\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# > <span style=\"color:green; font-weight:;\">自动压缩保存图片</span>","metadata":{}},{"cell_type":"code","source":"import os\nimport time\nimport zipfile\nimport random\ndirectory = f'{install_path}/stable-diffusion-webui/outputs'\noutput_directory = '/kaggle/working/历史生成/'\noutput_path = '/kaggle/working/archive.zip'  \nclass ImageCompressor:\n    def __init__(self, directory, output_path, save_time):\n        self.directory = directory\n        self.output_path = output_path\n        self.save_time = save_time\n    def _compress_single_image(self, zipf, filepath):\n        zipf.write(filepath, os.path.relpath(filepath, self.directory))\n    def compress_directory(self):\n        while True:\n            with zipfile.ZipFile(self.output_path, 'w', zipfile.ZIP_DEFLATED) as zipf:\n                for root, _, files in os.walk(self.directory):\n                    for file in files:\n                        if file.endswith(('.jpg', '.jpeg', '.png', '.tmp')):\n                            filepath = os.path.join(root, file)\n                            self._compress_single_image(zipf, filepath)\n                print(f\"每隔{self.save_time}秒保存一次图片到archive.zip\")\n                time.sleep(self.save_time)\n    def run(self):\n        while True:\n            time.sleep(0.5)\n            sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n            result = sock.connect_ex(('127.0.0.1', 7860))\n            if result == 0:\n                break\n            sock.close()\n        self.compress_directory()\ndef compress_images(directory, output_directory):\n    !mkdir /kaggle/working/历史生成/\n    initial_files = set()\n    for root, _, files in os.walk(directory):\n        for file in files:\n            if file.endswith(('.jpg', '.jpeg', '.png', '.tmp')):\n                filepath = os.path.join(root, file)\n                initial_files.add(filepath)\n    counter = 1 \n    while True:\n        time.sleep(0.1)\n        current_files = set()\n        for root, _, files in os.walk(directory):\n            for file in files:\n                if file.endswith(('.jpg', '.jpeg', '.png', '.tmp')):\n                    filepath = os.path.join(root, file)\n                    current_files.add(filepath)\n        new_files = current_files - initial_files\n        if new_files:\n            temperatures = get_gpu_temperature()\n            for i, temp in enumerate(temperatures):\n                print(f\"当前GPU Nvidia Tesla T4 {i+1} 温度: {temp}°C(温度越高，生成速度会稍微下降0.2%)\")\n            #output_filename = str(counter).zfill(8) + '.zip'  \n            #output_path = os.path.join(output_directory, output_filename)\n            #zipf = zipfile.ZipFile(output_path, 'w', zipfile.ZIP_DEFLATED)\n            #for file in new_files:\n            #    zipf.write(file, os.path.relpath(file, directory))\n            #zipf.close()  # 递增计数器\n            #initial_files = current_files\n            #counter += 1\ndef extract_all_zips(directory):\n    for root, _, files in os.walk(directory):\n        for file in files:\n            if file.endswith('.zip'):\n                filepath = os.path.join(root, file)\n                with zipfile.ZipFile(filepath, 'r') as zip_ref:\n                    zip_ref.extractall(root)\n                os.remove(filepath)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"--------------","metadata":{}},{"cell_type":"markdown","source":"# > <span style=\"color:green; font-weight:;\">SD-webui启动函数</span>","metadata":{}},{"cell_type":"code","source":"def iframe_thread_1(port):\n    while True:\n        time.sleep(0.5)\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        result = sock.connect_ex(('127.0.0.1', port))\n        if result == 0:\n            break\n        sock.close()\n    p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n    for line in p.stdout:\n        print(line.decode(), end='')\n        result = subprocess.run(['curl', 'ipv4.icanhazip.com'], capture_output=True, text=True)\n        print('你的公网IP地址是', result.stdout.strip())\n        print('或者直接从gradio公网链接进入Webui')\n        \ndef start_webui_1():\n    if use2:\n        install_Frpc('7861',frpconfigfile1,use_frpc1)\n        ngrok_start(ngrokTokenFile1,7861,'第二个webui',ngrok_use1)\n        !sleep 90\n        threading.Thread(target=iframe_thread_1, daemon=True, args=(7861,)).start()\n        %cd $install_path/stable-diffusion-webui\n        args1.append(f'--ckpt=models/Stable-diffusion/{usedCkpt1}')\n        if os.path.exists(Venvpath):\n            !/kaggle/working/opt/conda/envs/venv/bin/python3 launch.py {' '.join(args1)} --port 7861 --device-id=1\n    pass\n\ndef start_webui_0():\n    threading.Thread(target=iframe_thread, daemon=True, args=(7860,)).start()\n    %cd $install_path\n    install_Frpc('7860',frpconfigfile,use_frpc)\n    ngrok_start(ngrokTokenFile,7860,'第一个webui',ngrok_use)\n    %cd $install_path/stable-diffusion-webui\n    !mkdir models/lyco\n    args.append(f'--ckpt=models/Stable-diffusion/{usedCkpt}')\n    if os.path.exists(Venvpath):\n        !/kaggle/working/opt/conda/envs/venv/bin/python3 launch.py {' '.join(args)} \n    else:\n        print('发生了一点错误导致无法启动')\n\ndef iframe_thread(port):\n    while True:\n        time.sleep(0.5)\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        result = sock.connect_ex(('127.0.0.1', port))\n        if result == 0:\n            break\n        sock.close()\n    p = subprocess.Popen([\"lt\", \"--port\", \"{}\".format(port)], stdout=subprocess.PIPE)\n    for line in p.stdout:\n        print(line.decode(), end='')\n        result = subprocess.run(['curl', 'ipv4.icanhazip.com'], capture_output=True, text=True)\n        print('你的公网IP地址是', result.stdout.strip())\n        print('或者直接从gradio公网链接进入Webui')\n        \ndef start_webui():\n    print('正在启动SD-webui')\n    with ProcessPoolExecutor() as executor:\n        futures = []\n        for func in [start_webui_0, start_webui_1]:\n            futures.append(executor.submit(func))\n            time.sleep(1)\n        for future in futures:\n            future.result()\n            \ndef prepare():\n    if localtunnel:  \n        !apt-get update & npm install -g localtunnel\n    else:\n        os.system('apt-get update')\n    os.system('apt -y install -qq aria2')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def main():\n    startTicks = time.time()\n    prepare()\n    with ProcessPoolExecutor() as executor:\n        futures = []\n        for func in [install_webui, venv_install]:\n            futures.append(executor.submit(func))\n            time.sleep(0.5)\n        try:\n            for future in futures:\n                future.result()\n        except Exception as e:\n            print(\"运行出错了，请加群632428790解决。\")\n        except CancelledError:\n            print(\"运行被用户中止\")\n    libtcmalloc()\n    ticks = time.time()\n    print(\"加载耗时:\", (ticks - startTicks), \"s\")\n    try:\n        start_webui()\n    except Exception as e:\n        print(\"运行出错了，请联系管理员解决，错误信息:\")","metadata":{"ExecutionIndicator":{"show":false},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"-------------","metadata":{}},{"cell_type":"markdown","source":"# > <span style=\"color:green; font-weight:;\">打包图片上传到HuggingFace (可选)</span>","metadata":{}},{"cell_type":"code","source":"#功能函数，清理打包上传\nfrom pathlib import Path\nfrom huggingface_hub import HfApi, login\n\ndef zip_venv():\n        !pip install conda-pack\n        !rm -rf /kaggle/working/venv.tar.gz\n        !conda pack -n venv -o /kaggle/working/venv.tar.gz --compress-level 0\n\ndef hugface_upload(huggingface_token_file, yun_files, repo_id):\n    if Path(huggingface_token_file).exists():\n        with open(huggingface_token_file, encoding=\"utf-8\") as nkfile:\n            hugToken = nkfile.readline()\n        if hugToken != '':\n            # 使用您的 Hugging Face 访问令牌登录\n            login(token=hugToken)\n            # 实例化 HfApi 类\n            api = HfApi()\n            print(\"HfApi 类已实例化\")\n            %cd $install_path/stable-diffusion-webui\n            # 使用 upload_file() 函数上传文件\n            print(\"开始上传文件...\")\n            for yun_file in yun_files:\n                if Path(yun_file).exists():\n                    response = api.upload_file(\n                        path_or_fileobj=yun_file,\n                        path_in_repo=yun_file,\n                        repo_id=repo_id,\n                        repo_type=\"dataset\"\n                    )\n                    print(\"文件上传完成\")\n                    print(f\"响应: {response}\")\n                else:\n                    print(f'Error: File {yun_file} does not exist')\n    else:\n        print(f'Error: File {huggingface_token_file} does not exist')\n\ndef clean_folder(folder_path):\n    if not os.path.exists(folder_path):\n        return\n    for filename in os.listdir(folder_path):\n        file_path = os.path.join(folder_path, filename)\n        if os.path.isfile(file_path):\n            os.remove(file_path)\n        elif os.path.isdir(file_path):\n            shutil.rmtree(file_path)\n\ndef zip_clear_updata():\n    if zip_output:\n        output_folder = '/kaggle/outputs/'\n        if os.path.exists(output_folder):\n            shutil.make_archive('/kaggle/working/图片', 'zip', output_folder)\n            print('图片已压缩到output')\n        else:\n            print(f'文件夹 {output_folder} 不存在，跳过压缩操作')\n    if clear_output:\n        %cd /kaggle/outputs/\n        clean_folder('img2img-images')\n        clean_folder('txt2img-images')\n        clean_folder('img2img-grids')\n        clean_folder('txt2img-grids')\n        clean_folder('extras-images')\n        print('清理完毕')\n    if huggingface_use == True:\n        hugface_upload(huggingface_token_file,yun_files,huggiingface_repo_id)\n    if use_zip_venv == True:\n        zip_venv()\n        \n# 如果kaggle报错了，说明你笔记设置的ENVIRONMENT是2022年的旧版，要换成Always use latest environment\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# > <span style=\"color:red; font-weight:;\">执行区域，输出结果在此处看</span>","metadata":{}},{"cell_type":"markdown","source":"# > <span style=\"color:red; font-weight:;\">如果报错了，请反馈给群主</span>","metadata":{}},{"cell_type":"code","source":"import concurrent.futures\n'''\n执行函数\n'''\nif __name__ == \"__main__\":\n    compressor = ImageCompressor(directory=directory, output_path=output_path, save_time=60) #save_time为图片自动保存间隔，默认60秒压缩保存一次图片\n    executor = concurrent.futures.ThreadPoolExecutor(max_workers=2)\n    future1 = executor.submit(main)\n    future2 = executor.submit(compressor.run)\n    concurrent.futures.wait([future1, future2])\n    executor.shutdown()","metadata":{"_kg_hide-input":true,"_kg_hide-output":false,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!/kaggle/working/opt/conda/envs/venv/bin/python3 launch.py --xformers --administrator --share","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 这个代码是用来打包SadTalker生成的视频的，保存至SadTalker.zip里\n!zip -r /kaggle/working/SadTalker.zip  /kaggle/working/stable-diffusion-webui/results/*","metadata":{"trusted":true},"execution_count":null,"outputs":[{"name":"stdout","text":"每隔60秒保存一次图片到archive.zip\n每隔60秒保存一次图片到archive.zip\n每隔60秒保存一次图片到archive.zip\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### 运行之前请检查GPU和Internet是否已经打开\n### 如果出现报错，最有效的解决方法是先将PERSISTENCE改为No，再重新启动，相当于清除数据重新安装\n## 如果链接无法打开，请换内网穿透方式。(默认gradio内网穿透)\n## 生成的图片历史在Output目录里的archive.zip，也可以从webui的图库浏览器里看","metadata":{}},{"cell_type":"markdown","source":"# 有疑问加群632428790，免费答疑。分享模型\n# 写代码不易，希望给个打赏给我哦~ 爱发电：https://afdian.net/a/KaggleSD","metadata":{}},{"cell_type":"markdown","source":"## 我的其它一些Kaggle云端部署项目，感兴趣来看看：\n### Stable Diffusion ComfyUI：https://www.kaggle.com/code/qq2575044704/stable-diffusion-comfyui-sdxl\n### Lora训练：https://www.kaggle.com/code/qq2575044704/lora-train-kaggle-lora","metadata":{}},{"cell_type":"markdown","source":"# 使用帮助\n## kaggle账号\n- 注册账号需要手机号，国内手机号也行，如果点击注册后没反应，估计是需要梯子，用于人机验证\n- 注册后点此笔记的 **Copy & Edit** 按钮就进到编辑界面\n\n## **准备工作**\n1. 右侧面板 **Settings/ACCELERATOR** 需要选择GPU **P100 或 T4x2** 这两据说有差异，但我用起来差不多\n2. 右侧面板 **Settings/LANGUAGE** 需要选择Python\n2. 右侧面板 **Settings/PERSISTENCE** 建议选择 Files only **作用是保存Outpot目录内的文件**\n3. 右侧面板 **Settings/ENVIRONMENT** 建议不改这个配置，使用当前默认值就行\n4. 右侧面板 **Settings/INTERNET** 需要打开 用于联网，没网跑不起来的啊\n\n## **启动**\n#### 启动方式一  **直接点击页面上边的 RunAll**\n- 在没有关闭电源的情况下，后几次点击RunAll的输出在页面上端 （其实没有必要了，之前不知道代码块可以收起，很烦滚动到页面底端才能看见输出）\n- 手机端可能会出现页面上边的工具栏不显示的情况，左侧菜单按钮里也有相关的操作\n- 长时间不操作页面会导致脚本停止 （应该是40分钟吧）\n\n#### 启动方式二  **使用页面上边的 Save Version 后台运行**\n- 后台运行不用担心长时间不操作脚本停止\n- Version Type 选择 **Save & Run All**\n- 在Save Version弹窗里需要选择使用**GPU**环境 （Advanced Settings 里最后一个选项）\n- 后台运行的输出的图片可以在运行结束后下载（但是保存时间有限制，我就经常下不到，不够问题不大，喜欢的图在生成后就下载了）\n- 如果你需要下载运行后的图片，请不要把安装目录修改到 /kaggle/working 这个目录下，因为没有写打包功能，下载只能下载整个输出目录，也就是 /kaggle/working 目录\n\n## 访问\n- 如果你使用了ngrok或者frpc，可以访问你这两对应的地址\n- 如果你不知道你的ngrok或者frpc的地址可以在控制台（页面最下方Console）的输出里面查看\n- 使用Run All方式启动，控制台在启动完成后会输出访问网址，网址内容包含**gradio.live**，可以在页面中搜索快速找到\n- 如果使用Save Verson的方式启动，点击左下角的**View Active Events**点击刚刚启动的脚步，在**Log**里找访问网址\n- 一般情况下第一次启动此脚本需要等待kaggle下载模型文件，进度在页面上方\n- 第二次及以后（不增加新的文件）需要3到5分钟\n\n## **增加模型**\n# 方法一：\n通过下载连接下载到Kaggle\nKaggle的宽带很快，300MB/s，不到30秒就下好大模型了\n# 方法二：\n1. 先创建数据集，也就是dataset\n2. 创建时需要添加文件，选择自己的模型文件就行\n3. 同类型文件放相同的数据集里面，一个数据集也不要太大\n4. 可以在dataset搜索其他人上传的模型\n5. 通过右侧的 **Add Data** 按钮选择已经上传的模型文件或者别人上传的模型文件\n    - input 下面的列表就是模型文件，可以点击名称后面的复制按钮复制路径\n6. 将模型路径放在配置里的对应配置里即可，支持文件夹和文件路径，参考\n    - 如果目录里还有子目录也是需要加载的，可以用*表示子目录 例子：比如Loras目录下还有角色、画风、涩涩的文件夹，那路径里写成 '/kaggle/input/Loras/*'就可以加载子目录里面的文件了\n    - 模型加载使用的文件链接方式，如果你融模型的时候新模型名字和原有模型名字一样，会出现不能修改只读文件的错误\n    - 同理，直接对模型做编辑的工具可能也会出现相同的错误\n    \n  \n- **为了提高启动速度，导致切换模型过程较慢，点击切换模型后进度条大概率会一直存在，但模型在1分半左右基本能加载完。** \n- **受到kaggle内存大小的影响，切换多个模型后大概率爆内存导致停止运行**\n    \n**下边的配置项都写了对应配置的作用和使用说明，不理解的话也不用改，用默认的就好**\n\n## 下载文件\n#### 方式一\n- 在浏览器直接下 比如你需要下载的文件路径在 /kaggle/stable-diffusion-webui/models/Lora/dow_a.safetensors\n    - 比如你需要下载的文件路径在 /kaggle/stable-diffusion-webui/models/Lora/dow_a.safetensors\n    - 你的访问地址是 https://123123123.gradio.live\n    - 则可以在浏览器输入 https://123123123.gradio.live/file=/kaggle/stable-diffusion-webui/models/Lora/dow_a.safetensors 下载你的文件\n    \n#### 方式二\n- 复制到Output目录下载 仅支持使用Run All方式运行的\n    - 比如你需要下载的文件路径在 /kaggle/working/stable-diffusion-webui/models/Lora/dow_a.safetensors\n    - 先停止笔记本（不是关机，是停止）\n    - 然后新建一个代码块，在里面输入 !cp -f /kaggle/working/stable-diffusion-webui/models/Lora/dow_a.safetensors /kaggle/working/\n    - 就可以在右侧列表的Output目录看见复制出来的文件，点击下载即可\n    \n#### 方式三\n- 开启链接输出目录的配置 （配置在第二个代码块，通过搜索**配置文件链接**快速查找）\n    - 此方法会把已知的三个训练输出目录链接到Output目录下，直接去下载即可（两种启动方式都可以用）\n    - 如果有新的目录需要链接，可以参考着自己写或者联系我\n    \n#### 方式四\n- 将安装目录改到输出目录（配置在第二个代码块，通过搜索**安装目录**快速查找）\n    - 此方式会把所有文件都放在安装目录，找到并下载即可\n    - 如果使用这个方式，右侧的设置里**PERSISTENCE**这个设置项建议选No pensistence。如果选其他项，可能会出现关机特别慢的情况，因为需要上传输出目录的文件。\n\n## **一些可能没用的说明**\n\n## **一些可能没用的说明**\n- 配置说明 **True或者False**表示布尔值 **True**表示“**是**” **False**表示“**否**” 只有这两个值\n- 配置说明 **[]** 表示数组，里面可以存放内容，每个内容需要用**英语(半角)逗号**隔开\n- 配置说明 **''或者\"\"** 英语(半角)的双引号或者单引号包裹的内容是**字符串**，比如放在数组里面的路径就需要是一个字符串\n- 配置说明 **#** **#** 后面的内容是**注释**，是帮助性内容，对整个代码的执行不会有影响\n\n## **一些常见的错误**\n 1.Run All后白屏：可能是开了网页自动翻译导致，请重试\n \n 2.跑到一半出错了：更新到最新版本重新导入，下载地址 https://huggingface.co/datasets/ACCA225/Kaggle-Stable-Diffusion ，\n 如果还是出问题了，请联系管理员\n # 群号码：632428790","metadata":{}},{"cell_type":"markdown","source":"-----------------","metadata":{}},{"cell_type":"markdown","source":"# > <span style=\"color:green; font-weight:;\">附录：Webui启动参数</span>\n常见的：\n\n*\n --xformers 尝试使用xformers \n\n--force-enable-xformers 强制使用xformers\n\n\n  --xformers-flash-attention 启用具有Flash Attention的xformers\n  \n   --no-half-vae   VAE全精度(可以解决黑图问题)\n   \n --no-hashing 取消模型哈希计算值*","metadata":{}},{"cell_type":"code","source":"'''\n  -h, --help            显示此帮助消息并退出\n  --update-all-extensions\n                        launch.py 参数：在启动程序时下载所有扩展的更新\n  --skip-python-version-check\n                        launch.py 参数：不检查Python版本\n  --skip-torch-cuda-test\n                        launch.py 参数：不检查CUDA是否能正常工作\n  --reinstall-xformers  launch.py 参数：安装适当版本的xformers，即使您已经安装了某个版本\n  --reinstall-torch     launch.py 参数：安装适当版本的torch，即使您已经安装了某个版本\n  --update-check        launch.py 参数：在启动时检查更新\n  --test-server         launch.py 参数：配置用于测试的服务器\n  --skip-prepare-environment\n                        launch.py 参数：跳过所有环境准备步骤\n  --skip-install        launch.py 参数：跳过软件包的安装\n  --data-dir DATA_DIR   存储所有用户数据的基本路径\n  --config CONFIG       构建模型的配置文件路径\n  --ckpt CKPT           稳定扩散模型的检查点路径；如果指定了此参数，该检查点将添加到检查点列表并加载\n  --ckpt-dir CKPT_DIR   包含稳定扩散检查点的目录路径\n  --vae-dir VAE_DIR     包含VAE文件的目录路径\n  --gfpgan-dir GFPGAN_DIR\n                        GFPGAN目录\n  --gfpgan-model GFPGAN_MODEL\n                        GFPGAN模型文件名\n  --no-half             不将模型切换为16位浮点数\n  --no-half-vae         不将VAE模型切换为16位浮点数\n  --no-progressbar-hiding\n                        不在gradio UI中隐藏进度条（因为它会减慢浏览器中的硬件加速）\n  --max-batch-count MAX_BATCH_COUNT\n                        UI的最大批次计数值\n  --embeddings-dir EMBEDDINGS_DIR\n                        文本反演的嵌入目录（默认为embeddings）\n  --textual-inversion-templates-dir TEXTUAL_INVERSION_TEMPLATES_DIR\n                        包含文本反演模板的目录路径\n  --hypernetwork-dir HYPERNETWORK_DIR\n                        超网络目录\n  --localizations-dir LOCALIZATIONS_DIR\n                        本地化目录\n  --allow-code          允许从Web界面执行自定义脚本\n  --medvram             启用稳定扩散模型的优化，以牺牲一些速度以实现低VRM使用率\n  --lowvram             启用稳定扩散模型的优化，以牺牲大量速度以实现非常低的VRM使用率\n  --lowram              将稳定扩散检查点权重加载到VRAM而不是RAM中\n  --always-batch-cond-uncond\n                        禁用条件/非条件批处理，该批处理可通过--medvram或--lowvram来节省内存\n  --unload-gfpgan       无任何操作。\n  --precision {full,autocast}\n                        在此精度下进行评估\n  --upcast-sampling     上升采样。对于--no-half没有影响。通常与--no-half相比，产生类似的结果，性能更好，同时使用更少的内存。\n  --share               对gradio使用share=True，并使UI可以通过其网站访问\n  --ngrok NGROK         ngrok的认证令牌，替代gradio --share\n  --ngrok-region NGROK_REGION\n                        无任何操作。\n  --ngrok-options NGROK_OPTIONS\n                        以JSON格式传递给ngrok的选项，例如：\n                        '{\"authtoken_from_env\":true,\n                        \"basic_auth\":\"user:password\",\n                        \"oauth_provider\":\"google\",\n                        \"oauth_allow_emails\":\"user@asdf.com\"}'\n  --enable-insecure-extension-access\n                        禁用其他选项，启用扩展选项\n  --codeformer-models-path CODEFORMER_MODELS_PATH\n                        包含codeformer模型文件的目录路径。\n  --gfpgan-models-path GFPGAN_MODELS_PATH\n                        包含GFPGAN模型文件的目录路径。\n  --esrgan-models-path ESRGAN_MODELS_PATH\n                        包含ESRGAN模型文件的目录路径。\n  --bsrgan-models-path BSRGAN_MODELS_PATH\n                        包含BSRGAN模型文件的目录路径。\n  --realesrgan-models-path REALESRGAN_MODELS_PATH\n                        包含RealESRGAN模型文件的目录路径。\n  --clip-models-path CLIP_MODELS_PATH\n                        包含CLIP模型文件的目录路径。\n  --xformers            启用xformers的交叉注意力层\n  --force-enable-xformers\n                        启用xformers的交叉注意力层，无论检查代码是否认为您可以运行它；如果此操作无法正常工作，请不要提交错误报告\n  --xformers-flash-attention\n                        启用具有Flash Attention的xformers，以提高可重现性（仅适用于SD2.x或变体）\n  --deepdanbooru        无任何操作。\n  --opt-split-attention\n                        首选Doggettx的交叉注意力层优化，用于自动选择优化方式\n  --opt-sub-quad-attention\n                        首选内存高效的次二次交叉注意力层优化，用于自动选择优化方式\n  --sub-quad-q-chunk-size SUB_QUAD_Q_CHUNK_SIZE\n                        用于次二次交叉注意力层优化的查询块大小\n  --sub-quad-kv-chunk-size SUB_QUAD_KV_CHUNK_SIZE\n                        用于次二次交叉注意力层优化的kv块大小\n  --sub-quad-chunk-threshold SUB_QUAD_CHUNK_THRESHOLD\n                        用于次二次交叉注意力层优化的VRAM阈值的百分比，以使用块处理\n  --opt-split-attention-invokeai\n                        首选InvokeAI的交叉注意力层优化，用于自动选择优化方式\n  --opt-split-attention-v1\n                        首选旧版本的分割注意力优化，用于自动选择优化方式\n  --opt-sdp-attention   首选缩放点积交叉注意力层优化，用于自动选择优化方式；需要PyTorch 2.*\n  --opt-sdp-no-mem-attention\n                        首选没有内存高效注意力的缩放点积交叉注意力层优化，用于自动选择优化方式，使图像生成具有确定性；需要PyTorch 2.*\n  --disable-opt-split-attention\n                        首选不进行交叉注意力层优化，用于自动选择优化方式\n  --disable-nan-check   不检查生成的图像/潜空间是否包含NaN；在没有检查点的情况下运行时很有用\n  --use-cpu USE_CPU [USE_CPU ...]\n                        使用CPU作为指定模块的torch设备\n  --listen              使用0.0.0.0作为服务器名称启动gradio，以响应网络请求\n  --port PORT           使用给定的服务器端口启动gradio，对于<1024的端口，您需要root/admin权限，默认为7860（如果可用）\n  --show-negative-prompt\n                        无任何操作。\n  --ui-config-file UI_CONFIG_FILE\n                        用于ui配置的文件名\n  --hide-ui-dir-config  隐藏Web界面中的目录配置\n  --freeze-settings     禁用编辑设置\n  --ui-settings-file UI_SETTINGS_FILE\n                        用于ui设置的文件名\n  --gradio-debug        使用--debug选项启动gradio\n  --gradio-auth GRADIO_AUTH\n                        设置gradio的身份验证，格式为“username:password”；或者使用逗号分隔多个，例如“u1:p1,u2:p2,u3:p3”\n  --gradio-auth-path GRADIO_AUTH_PATH\n                        设置gradio的身份验证文件路径，例如“/path/to/auth/file”，与--gradio-auth具有相同的身份验证格式\n  --gradio-img2img-tool GRADIO_IMG2IMG_TOOL\n                        无任何操作。\n  --gradio-inpaint-tool GRADIO_INPAINT_TOOL\n                        无任何操作。\n  --gradio-allowed-path GRADIO_ALLOWED_PATH\n                        将路径添加到gradio的allowed_paths，使其可以从中提供文件\n  --opt-channelslast    将稳定扩散的内存类型更改为channels last\n  --styles-file STYLES_FILE\n                        用于样式的文件名\n  --autolaunch          启动后在系统的默认浏览器中打开Web界面的URL\n  --theme THEME         使用浅色或深色主题启动UI\n  --use-textbox-seed    在UI中使用文本框作为种子（没有上/下箭头，但可以输入长种子）\n  --disable-console-progressbars\n                        不将进度条输出到控制台\n  --enable-console-prompts\n                        使用txt2img和img2img生成时，在控制台打印提示\n  --vae-path VAE_PATH   用作VAE的检查点；设置此参数会禁用与VAE相关的所有设置\n  --disable-safe-unpickle\n                        禁用检查PyTorch模型是否包含恶意代码\n  --api                 使用api=True同时启动API和Web界面（仅使用--nowebui启动API）\n  --api-auth API_AUTH   设置API的身份验证，格式为“username:password”；或者使用逗号分隔多个，例如“u1:p1,u2:p2,u3:p3”\n  --api-log             使用api-log=True启用所有API请求的日志记录\n  --nowebui             使用api=True启动API而不是Web界面\n  --ui-debug-mode       不加载模型，快速启动UI\n  --device-id DEVICE_ID\n                        选择要使用的默认CUDA设备（在之前需要导出CUDA_VISIBLE_DEVICES=0,1等）\n  --administrator       管理员权限\n  --cors-allow-origins CORS_ALLOW_ORIGINS\n                        以逗号分隔的列表形式的允许CORS源（无空格）\n  --cors-allow-origins-regex CORS_ALLOW_ORIGINS_REGEX\n                        单个正则表达式形式的允许CORS源\n  --tls-keyfile TLS_KEYFILE\n                        部分启用TLS，需要--tls-certfile才能完全工作\n  --tls-certfile TLS_CERTFILE\n                        部分启用TLS，需要--tls-keyfile才能完全工作\n  --disable-tls-verify  通过此参数启用使用自签名证书。\n  --server-name SERVER_NAME\n                        设置服务器的主机名\n  --gradio-queue        无任何操作。\n  --no-gradio-queue     禁用gradio队列；导致网页使用HTTP请求而不是Websockets；在早期版本中是默认设置\n  --skip-version-check  不检查torch和xformers的版本\n  --no-hashing          禁用检查点的sha256哈希，以提高加载性能\n  --no-download-sd-model\n                        即使在--ckpt-dir中找不到模型，也不下载SD1.5模型\n  --subpath SUBPATH     自定义gradio的子路径，与反向代理一起使用\n  --add-stop-route      添加/_stop路由以停止服务器\n  '''","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"---------------","metadata":{}},{"cell_type":"markdown","source":"# 注意事项/WARNING：\n- ### 1.将设置中的PERSISTENCE改为Files Only方便下次打开提高启动速度、\n- ### 2.检测到出现涩图会容易导致封号\n- ### 3.如果不能启动，请新建一个notebook并且重新导入\n- ### 4.若出现BUG，请跟我们反馈\n# NoteBook Created By 2575044704\n# Stable Diffusion By AUTOMATIC1111\n# DO NOT PRODUCE NSFW IMAGE!! it violates the Kaggle rules, learn more: https://www.kaggle.com/community-guidelines","metadata":{}},{"cell_type":"markdown","source":"\n<div class=\"alert alert-block alert-info\" style=\"font-size:14px; font-family:verdana;\">\n    📌 2022年11月18日: Created By Yiyiooo\n</div>\n最近更新日志：\n<div class=\"alert alert-block alert-info\" style=\"font-size:14px; font-family:verdana;\">\n     2023年3月5日更新：现在支持通过下载链接上传模型了，省去了下载模型后再上传后的麻烦.（）\n</div>\n<div class=\"alert alert-block alert-info\" style=\"font-size:14px; font-family:verdana;\">\n     2023年5月15日更新：现在可以双开webui了，可以双线程跑图（GPU请选择 T4 x2 ， 将use2设置为True）\n</div>\n<div class=\"alert alert-block alert-info\" style=\"font-size:14px; font-family:verdana;\">\n     2023年5月15日更新：更新了多线程启动，启动速度更快一些\n</div>\n<div class=\"alert alert-block alert-info\" style=\"font-size:14px; font-family:verdana;\">\n     2023年6月6日更新：更新了xformers版本，生成速度更快一些\n</div>\n<div class=\"alert alert-block alert-info\" style=\"font-size:14px; font-family:verdana;\">\n     2023年7月19日更新：更新了图片自动打包和删除功能，顺便添加了一些注释\n</div>\n<div class=\"alert alert-block alert-info\" style=\"font-size:14px; font-family:verdana;\">\n     2023年7月21日更新：更新了默认Cross Attention有哈启动参数，据说可以加快20%生成速度，同时现在可以显示GPU温度了\n</div>","metadata":{}}]}
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